Calibrating system for colorizing point-clouds

ABSTRACT

A system includes a three-dimensional (3D) scanner that captures a 3D point cloud corresponding to one or more objects in a surrounding environment. The system further includes a camera that captures a control image by capturing a plurality of images of the surrounding environment, and an auxiliary camera configured to capture an ultrawide-angle image of the surrounding environment. One or more processors of the system colorize the 3D point cloud using the ultrawide-angle image by mapping the ultrawide-angle image to the 3D point cloud. The system performs a limited system calibration before colorizing each 3D point cloud, and a periodic full system calibration before/after a plurality of 3D point clouds are colorized.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 63/180,285, filed Apr. 27, 2021, the entire disclosure of which isincorporated herein by reference.

BACKGROUND

The subject matter disclosed herein relates to use of athree-dimensional (3D) measurement devices to capture 3D point clouds(point clouds), and particularly to 3D measurement devices that includea 3D scanner to capture the point clouds and a 2D camera to capturecolor data for the point clouds. More specifically, technical solutionsare described herein to improve colorization of the point clouds byimproving calibration between the 2D camera and the 3D scanner.

Typically, the 3D scanner is a time-of-flight (TOF) laser scanner, whichsteers a beam of light to a non-cooperative target such as a diffuselyscattering surface of an object. A distance meter in the device measuresa distance to the object, and angular encoders measure the angles ofrotation of two axles in the device. The measured distance and twoangles enable a processor in the device to determine the 3D coordinatesof the target.

A TOF laser scanner is a scanner in which the distance to a target pointis determined based on the speed of light in air between the scanner anda target point. Laser scanners are typically used for scanning closed oropen spaces such as interior areas of buildings, industrialinstallations and tunnels. They may be used, for example, in industrialapplications and accident reconstruction applications. A laser scanneroptically scans and measures objects in a volume around the scannerthrough the acquisition of data points representing object surfaceswithin the volume. Such data points are obtained by transmitting a beamof light onto the objects and collecting the reflected or scatteredlight to determine the distance, two-angles (i.e., an azimuth and azenith angle), and optionally a gray-scale value. This raw scan data iscollected, stored and sent to a processor or processors to generate a 3Dimage representing the scanned area or object.

Generating the 3D image requires at least three values for each datapoint. These three values may include the distance and two angles, ormay be transformed values, such as the x, y, z coordinates. In anembodiment, the 3D image is also based on a fourth gray-scale value,which is a value related to irradiance of scattered light returning tothe scanner.

Most TOF scanners direct the beam of light within the measurement volumeby steering the light with a beam steering mechanism. The beam steeringmechanism includes a first motor that steers the beam of light about afirst axis by a first angle that is measured by a first angular encoder(or another angle transducer). The beam steering mechanism also includesa second motor that steers the beam of light about a second axis by asecond angle that is measured by a second angular encoder (or anotherangle transducer).

Many contemporary laser scanners include a 2D camera, such as a colorcamera, mounted on the laser scanner for gathering camera digital imagesof the environment and for presenting the camera digital images to anoperator of the laser scanner. By viewing the camera images, theoperator of the scanner can determine the field of view of the measuredvolume and adjust settings on the laser scanner to measure over a largeror smaller region of space. In addition, the camera digital images maybe transmitted to a processor to add color to the scanner 3D image. Togenerate a color scanner image, at least three positional coordinates(such as x, y, z) and three color values (such as red, green, blue“RGB”) are collected for each data point.

Some 3D scanners use high dynamic range (HDR) techniques with the colorcamera to provide enhanced color images that used with the scanner imageto provide a more accurate color representation of the scannedenvironment. HDR techniques involve acquiring multiple images at eachlocation with different exposure settings. These images are thencombined to provide a resulting image that more accurately representsthe environment. Another option for HDR, sometimes named interferencemode, is to apply different exposure times to different parts of onecaptured image. This technique is useful in areas having high contrast(light and dark areas). While HDR images are certainly useful inenhancing the color of the scanner image, the acquiring of multipleimages at different exposures can be time consuming. For example, toacquire images in a 360 spherical area about the 3D scanner may takemore than 60 images. If each of these 60 images has multiple exposures,then the time to acquire all of the images may be lengthy.

Accordingly, while existing 3D scanners are suitable for their intendedpurposes, what is needed is a 3D scanner having certain features ofembodiments of the present disclosure.

BRIEF DESCRIPTION

A system includes a three-dimensional (3D) scanner that captures a 3Dpoint cloud corresponding to one or more objects in a surroundingenvironment. The system further includes a camera that captures acontrol image by capturing a plurality of images of the surroundingenvironment, and an auxiliary camera configured to capture anultrawide-angle image of the surrounding environment. One or moreprocessors of the system colorize the 3D point cloud using theultrawide-angle image by mapping the ultrawide-angle image to the 3Dpoint cloud. The system performs a limited system calibration beforecolorizing each 3D point cloud, and a periodic full system calibrationbefore/after a plurality of 3D point clouds are colorized.

The full system calibration calibrates the 3D scanner, the camera, andthe auxiliary camera using the 3D point cloud, the control image, and acalibration image, which is another ultrawide-angle image from theauxiliary image.

The full system calibration includes extracting a first plurality offeatures from the control image using a feature-extraction algorithm.The full system calibration further includes extracting a secondplurality of features from the calibration image using thefeature-extraction algorithm. The full system calibration furtherincludes determining a set of matching features from the first pluralityof features and the second plurality of features by using afeature-matching algorithm. The full system calibration further includesbuilding control points by using the set of matching features and thelaser scan 3D point cloud. The full system calibration further includesdetermining all system calibration parameters on-the-fly through usingbundle adjustment and camera self-calibration.

The limited system calibration is performed before colorizing the 3Dpoint cloud.

The limited system calibration updates a subset from the set of systemcalibration parameters.

The limited system calibration includes extracting a third plurality offeatures from the control image using the feature-extraction algorithm.The limited system calibration further includes determining another setof matching features to the extracted features at the first plurality offeatures by using a hybrid feature matching algorithm. The limitedsystem calibration further includes building control points by using theanother set of matching features and the 3D point cloud. The limitedsystem calibration further includes determining fully or partiallyupdated values for at least one of the system calibration parameterson-the-fly through bundle adjustment and camera self-calibration.

Two successive full system calibrations are performed after apredetermined interval.

In some embodiments, successive full system calibrations are performedafter a predetermined number of 3D point clouds are colorized.

In some embodiments, extracting the second plurality of features fromthe calibration image includes transforming the calibration image to aspherical image, and extracting the second plurality of features fromthe spherical image.

In some embodiments, the auxiliary camera includes two lenses atpredetermined offsets relative to each other.

In some embodiments, determining the points in the 3D point cloud thatare corresponding to the set of matching features is performed usingbilinear interpolation.

In some embodiments, the set of system calibration parameters includes afirst plurality of camera calibration parameters, a second plurality ofdual-camera calibration parameters, and a third plurality ofmulti-device orientation parameters.

In some embodiments, the camera is an integral part of the 3D scanner.

In some embodiments, the auxiliary camera is mounted on the 3D scannerat a predetermined position relative to the 3D scanner.

According to some aspects described herein, a method includesperiodically performing a full system calibration of a measurementdevice that comprises a 3D scanner, a camera, and an auxiliary camera.The method further includes performing a limited system calibration ofthe measurement device in response to capturing a second 3D point cloudthat is to be colorized, wherein the limited system calibrationcomprises updating a subset from the set of system calibrationparameters using the second 3D point cloud and a second ultrawide-angleimage from the auxiliary camera.

According to some aspects described herein, a computer program productincludes one or more memory devices with computer executableinstructions stored thereon, the computer executable instructions whenexecuted by one or more processors cause the one or more processors toperform a method. The method includes periodically performing a fullsystem calibration of a measurement device that comprises a 3D scanner,a camera, and an auxiliary camera. The method further includesperforming a limited system calibration of the measurement device inresponse to capturing a second 3D point cloud that is to be colorized,wherein the limited system calibration comprises updating a subset fromthe set of system calibration parameters using the second 3D point cloudand a second ultrawide-angle image from the auxiliary camera.

These and other advantages and features will become more apparent fromthe following description taken in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The subject matter, which is regarded as the invention, is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a perspective view of a laser scanner in accordance with anembodiment;

FIG. 2 is a side view of the laser scanner illustrating a method ofmeasurement according to an embodiment;

FIG. 3 is a schematic illustration of the optical, mechanical, andelectrical components of the laser scanner according to an embodiment;

FIG. 4 illustrates a schematic illustration of the laser scanner of FIG.1 according to an embodiment;

FIG. 5 depicts different types of system calibration parametersaccording to one or more embodiments;

FIG. 6 is a flow diagram of a method of generating enhanced color scanswith the laser scanner of FIG. 1;

FIG. 7 depicts the projection types of ultrawide-angle (fisheye) lensand the path of light from a point in 3D space into the image plane.

FIG. 8 shows the relation of radius and zenith angle (and reverse) forall ultrawide angle (fisheye) lens types at FIG. 7 including also theperspective projection;

FIG. 9 depicts a table that provides the domains and ranges of varioustrigonometric functions;

FIG. 10 depicts a flowchart for a dynamic construction of control pointsfor calibrating the auxiliary image acquisition device according to oneor more embodiments;

FIG. 11 depicts example control image and calibration image used for adynamic construction of control points for calibrating the auxiliaryimage acquisition device according to one or more embodiments;

FIG. 12 shows matching features between a control image and acalibration image in an example scenario according to one or moreembodiments;

FIG. 13 depicts an example result in an example scenario according toone or more embodiments;

FIG. 14 depicts a flowchart of a method for using data captured by ameasurement device using on-the-fly calibration according to one or moreembodiments;

FIG. 15 provides a visual depiction of an example timeline of a workflowfor performing the method of FIG. 14;

FIG. 16 depicts an example scenario with 3D point cloud being colorizedwithout a limited system calibration according to one or moreembodiments; and

FIG. 17 depicts an example scenario with 3D point cloud being colorizedwith a limited system calibration according to one or more embodiments.

The detailed description explains embodiments of the invention, togetherwith advantages and features, by way of example with reference to thedrawings.

DETAILED DESCRIPTION

Embodiments herein relate to a 3D measuring device having a 3D scannerand at least one camera to capture color images. The camera, in someembodiments, is a dual-ultrawide-angle lens camera. Embodiments of thetechnical solutions described herein provide advantages to acquiringthree-dimensional (3D) coordinates of an area of the environment,acquiring a 2D color image of that area using the camera, andcalibrating the 3D scanner and the 2D camera (system calibration) tofacilitate mapping of the 2D image to the 3D coordinates. The result isan interactive 3D image of the area that includes the captured 3Dcoordinates and color. Embodiments provide advantages to perform areliable system calibration.

By using the dual-ultrawide-angle lens camera facilitates reducing thetime to acquiring the color images and colorizing the captured 3Dcoordinates, particularly in comparison to all existing techniques. Inthe existing techniques, the scanner system rotates (vertically andhorizontally) to different viewing directions and the camera capturesimages. Depending on the camera field of view, the number of imagecapture varies. For a typical technological case, a built-in camera witha nodal point, which is identical to the laser scanner nodal point (noparallax), takes many images due to a narrow camera field of view. Inother technological cases, in which the camera has a parallax to thelaser scanner, a wider field of view camera is used. Using embodimentsherein, a lower number of images are required in comparison to thebuilt-in camera and external wide-angle camera techniques to cover 360°environment.

Further, a technical challenge with 3D measuring devices is that usingcollinearity conditions alone cannot precisely model the mapping of 3Dpoints into an image space associated with the camera because ofsystematic errors. Typically, a sensor model, specific to the camera, isused to map the 3D points with the image space of the camera. However,the sensor model is based on one or more parameters that have to bedetermined and calibrated for the camera and 3D scanner to work togetherto provide precise 3D scans.

Referring now to FIGS. 1-3, a laser scanner 20 is shown for opticallyscanning and measuring the environment surrounding the laser scanner 20.The laser scanner 20 has a measuring head 22 and a base 24. Themeasuring head 22 is mounted on the base 24 such that the laser scanner20 may be rotated about a vertical axis 23. In one embodiment, themeasuring head 22 includes a gimbal point 27 that is a center ofrotation about the vertical axis 23 and a horizontal axis 25. Themeasuring head 22 has a rotary mirror 26, which may be rotated about thehorizontal axis 25. The rotation about the vertical axis may be aboutthe center of the base 24. The terms vertical axis and horizontal axisrefer to the scanner in its normal upright position. It is possible tooperate a 3D coordinate measurement device on its side or upside down,and so to avoid confusion, the terms azimuth axis and zenith axis may besubstituted for the terms vertical axis and horizontal axis,respectively. The term pan axis or standing axis may also be used as analternative to vertical axis.

The measuring head 22 is further provided with an electromagneticradiation emitter, such as light emitter 28, for example, that emits anemitted light beam 30. In one embodiment, the emitted light beam 30 is acoherent light beam such as a laser beam. The laser beam may have awavelength range of approximately 300 to 1600 nanometers, for example790 nanometers, 905 nanometers, 1550 nm, or less than 400 nanometers. Itshould be appreciated that other electromagnetic radiation beams havinggreater or smaller wavelengths may also be used. The emitted light beam30 is amplitude or intensity modulated, for example, with a sinusoidalwaveform or with a rectangular waveform. The emitted light beam 30 isemitted by the light emitter 28 onto a beam steering unit, such asmirror 26, where it is deflected to the environment. A reflected lightbeam 32 is reflected from the environment by an object 34. The reflectedor scattered light is intercepted by the rotary mirror 26 and directedinto a light receiver 36. The directions of the emitted light beam 30and the reflected light beam 32 result from the angular positions of therotary mirror 26 and the measuring head 22 about the axes 25 and 23,respectively. These angular positions in turn depend on thecorresponding rotary drives or motors.

Coupled to the light emitter 28 and the light receiver 36 is acontroller 38. The controller 38 determines, for a multitude ofmeasuring points X (FIG. 2), a corresponding number of distances dbetween the laser scanner 20 and the points X on object 34. The distanceto a particular point X is determined based at least in part on thespeed of light in air through which electromagnetic radiation propagatesfrom the device to the object point X. In one embodiment the phase shiftof modulation in light emitted by the laser scanner 20 and the point Xis determined and evaluated to obtain a measured distance d.

The speed of light in air depends on the properties of the air such asthe air temperature, barometric pressure, relative humidity, andconcentration of carbon dioxide. Such air properties influence the indexof refraction n of the air. The speed of light in air is equal to thespeed of light in vacuum c divided by the index of refraction. In otherwords, c_(air)=c/n. A laser scanner of the type discussed herein isbased on the time-of-flight (TOF) of the light in the air (theround-trip time for the light to travel from the device to the objectand back to the device). Examples of TOF scanners include scanners thatmeasure round trip time using the time interval between emitted andreturning pulses (pulsed TOF scanners), scanners that modulate lightsinusoidally and measure phase shift of the returning light (phase-basedscanners), as well as many other types. A method of measuring distancebased on the time-of-flight of light depends on the speed of light inair and is therefore easily distinguished from methods of measuringdistance based on triangulation. Triangulation-based methods involveprojecting light from a light source along a particular direction andthen intercepting the light on a camera pixel along a particulardirection. By knowing the distance between the camera and the projectorand by matching a projected angle with a received angle, the method oftriangulation enables the distance to the object to be determined basedon one known length and two known angles of a triangle. The method oftriangulation, therefore, does not directly depend on the speed of lightin air.

In one mode of operation, the scanning of the volume around the laserscanner 20 takes place by rotating the rotary mirror 26 relativelyquickly about axis 25 while rotating the measuring head 22 relativelyslowly about axis 23, thereby moving the assembly in a spiral pattern.In an exemplary embodiment, the rotary mirror rotates at a maximum speedof 5820 revolutions per minute. For such a scan, the gimbal point 27defines the origin of the local stationary reference system. The base 24rests in this local stationary reference system.

In addition to measuring a distance d from the gimbal point 27 to anobject point X, the scanner 20 may also collect gray-scale informationrelated to the received intensity (equivalent to the term “brightness”or “optical power”) value. The gray-scale value may be determined atleast in part, for example, by integration of the bandpass-filtered andamplified signal in the light receiver 36 over a measuring periodattributed to the object point X. As will be discussed in more detailherein, the intensity value may be used to enhance color images that areused to colorize the scanned data.

The measuring head 22 may include a display device 40 integrated intothe laser scanner 20. The display device 40 may include a graphicaltouch screen 41, as shown in FIG. 1, which allows the operator to setthe parameters or initiate the operation of the laser scanner 20. Forexample, the screen 41 may have a user interface that allows theoperator to provide measurement instructions to the device, and thescreen may also display measurement results.

The laser scanner 20 includes a carrying structure 42 that provides aframe for the measuring head 22 and a platform for attaching thecomponents of the laser scanner 20. In one embodiment, the carryingstructure 42 is made from a metal such as aluminum. The carryingstructure 42 includes a traverse member 44 having a pair of walls 46, 48on opposing ends. The walls 46, 48 are parallel to each other and extendin a direction opposite the base 24. Shells 50, 52 are coupled to thewalls 46, 48 and cover the components of the laser scanner 20. In theexemplary embodiment, the shells 50, 52 are made from a plasticmaterial, such as polycarbonate or polyethylene for example. The shells50, 52 cooperate with the walls 46, 48 to form a housing for the laserscanner 20.

On an end of the shells 50, 52 opposite the walls 46, 48 a pair of yokes54, 56 are arranged to partially cover the respective shells 50, 52. Inthe exemplary embodiment, the yokes 54, 56 are made from a suitablydurable material, such as aluminum for example, that assists inprotecting the shells 50, 52 during transport and operation. The yokes54, 56 each includes a first arm portion 58 that is coupled, such aswith a fastener for example, to the traverse 44 adjacent the base 24.The arm portion 58 for each yoke 54, 56 extends from the traverse 44obliquely to an outer corner of the respective shell 50, 52. From theouter corner of the shell, the yokes 54, 56 extend along the side edgeof the shell to an opposite outer corner of the shell. Each yoke 54, 56further includes a second arm portion that extends obliquely to thewalls 46, 48. It should be appreciated that the yokes 54, 56 may becoupled to the traverse 42, the walls 46, 48 and the shells 50, 54 atmultiple locations.

The pair of yokes 54, 56 cooperate to circumscribe a convex space withinwhich the two shells 50, 52 are arranged. In the exemplary embodiment,the yokes 54, 56 cooperate to cover all of the outer edges of the shells50, 54, while the top and bottom arm portions project over at least aportion of the top and bottom edges of the shells 50, 52. This providesadvantages in protecting the shells 50, 52 and the measuring head 22from damage during transportation and operation. In other embodiments,the yokes 54, 56 may include additional features, such as handles tofacilitate the carrying of the laser scanner 20 or attachment points foraccessories for example.

On top of the traverse 44, a prism 60 is provided. The prism extendsparallel to the walls 46, 48. In the exemplary embodiment, the prism 60is integrally formed as part of the carrying structure 42. In otherembodiments, the prism 60 is a separate component that is coupled to thetraverse 44. When the mirror 26 rotates, during each rotation the mirror26 directs the emitted light beam 30 onto the traverse 44 and the prism60. Due to non-linearities in the electronic components, for example inthe light receiver 36, the measured distances d may depend on signalstrength, which may be measured in optical power entering the scanner oroptical power entering optical detectors within the light receiver 36,for example. In an embodiment, a distance correction is stored in thescanner as a function (possibly a nonlinear function) of distance to ameasured point and optical power (generally unscaled quantity of lightpower sometimes referred to as “brightness”) returned from the measuredpoint and sent to an optical detector in the light receiver 36. Sincethe prism 60 is at a known distance from the gimbal point 27, themeasured optical power level of light reflected by the prism 60 may beused to correct distance measurements for other measured points, therebyallowing for compensation to correct for the effects of environmentalvariables such as temperature. In the exemplary embodiment, theresulting correction of distance is performed by the controller 38.

In an embodiment, the base 24 is coupled to a swivel assembly (notshown) such as that described in commonly owned U.S. Pat. No. 8,705,012('012), which is incorporated by reference herein. The swivel assemblyis housed within the carrying structure 42 and includes a motor 138 thatis configured to rotate the measuring head 22 about the axis 23. In anembodiment, the angular/rotational position of the measuring head 22about the axis 23 is measured by angular encoder 134.

An auxiliary image acquisition device 66 may be a device that capturesand measures a parameter associated with the scanned area or the scannedobject and provides a signal representing the measured quantities overan image acquisition area. The auxiliary image acquisition device 66 maybe, but is not limited to, a pyrometer, a thermal imager, an ionizingradiation detector, or a millimeter-wave detector. In an embodiment, theauxiliary image acquisition device 66 is a color camera with anultrawide-angle lens, sometimes referred to as a “fisheye camera.”

In an embodiment, a camera 112 is located internally to the scanner (seeFIG. 3) and may have the same optical axis as the 3D scanner device. Inthis embodiment, the camera 112 is integrated into the measuring head 22and arranged to acquire images along the same optical pathway as emittedlight beam 30 and reflected light beam 32. In this embodiment, the lightfrom the light emitter 28 reflects off a fixed mirror 116 and travels todichroic beam-splitter 118 that reflects the light 117 from the lightemitter 28 onto the rotary mirror 26. In an embodiment, the mirror 26 isrotated by a motor 136 and the angular/rotational position of the minoris measured by angular encoder 134. The dichroic beam-splitter 118allows light to pass through at wavelengths different than thewavelength of light 117. For example, the light emitter 28 may be a nearinfrared laser light (for example, light at wavelengths of 780 nm or1150 nm), with the dichroic beam-splitter 118 configured to reflect theinfrared laser light while allowing visible light (e.g., wavelengths of400 to 700 nm) to transmit through. In other embodiments, thedetermination of whether the light passes through the beam-splitter 118or is reflected depends on the polarization of the light. The camera 112obtains 2D images of the scanned area to capture color data to add tothe captured point cloud. In the case of a built-in color camera havingan optical axis coincident with that of the 3D scanning device, thedirection of the camera view may be easily obtained by simply adjustingthe steering mechanisms of the scanner—for example, by adjusting theazimuth angle about the axis 23 and by steering the mirror 26 about theaxis 25.

Referring now to FIG. 4 with continuing reference to FIGS. 1-3, elementsare shown of the laser scanner 20. Controller 38 is a suitableelectronic device capable of accepting data and instructions, executingthe instructions to process the data, and presenting the results. Thecontroller 38 includes one or more processing elements 122. Theprocessors may be microprocessors, field programmable gate arrays(FPGAs), digital signal processors (DSPs), and generally any devicecapable of performing computing functions. The one or more processors122 have access to memory 124 for storing information.

Controller 38 is capable of converting the analog voltage or currentlevel provided by light receiver 36 into a digital signal to determine adistance from the laser scanner 20 to an object in the environment.Controller 38 uses the digital signals that act as input to variousprocesses for controlling the laser scanner 20. The digital signalsrepresent one or more laser scanner 20 data including but not limited todistance to an object, images of the environment, images acquired by thecamera 112, angular/rotational measurements by a first or azimuthencoder 132, and angular/rotational measurements by a second axis orzenith encoder 134.

In general, controller 38 accepts data from encoders 132, 134, lightreceiver 36, light source 28, and camera 112 and is given certaininstructions for the purpose of generating a 3D point cloud of a scannedenvironment. Controller 38 provides operating signals to the lightsource 28, light receiver 36, camera 112, zenith motor 136, and azimuthmotor 138. In one or more embodiments, the controller 38 also providesoperating signals to the auxiliary image acquisition device 66. Thecontroller 38 compares the operational parameters to predeterminedvariances and if the predetermined variance is exceeded, generates asignal that alerts an operator to a condition. The data received by thecontroller 38 may be displayed on a user interface 40 coupled tocontroller 38. The user interface 40 may be one or more LEDs(light-emitting diodes) 82, an LCD (liquid-crystal diode) display, a CRT(cathode ray tube) display, a touchscreen display or the like. A keypadmay also be coupled to the user interface for providing data input tocontroller 38. In one embodiment, the user interface is arranged orexecuted on a mobile computing device that is coupled for communication,such as via a wired or wireless communications medium (e.g. Ethernet,serial, USB, Bluetooth™ or WiFi) for example, to the laser scanner 20.

The controller 38 may also be coupled to external computer networks suchas a local area network (LAN) and the Internet. A LAN interconnects oneor more remote computers, which are configured to communicate withcontroller 38 using a well-known computer communications protocol suchas TCP/IP (Transmission Control Protocol/Internet Protocol), RS-232,ModBus, and the like. Additional systems 20 may also be connected to LANwith the controllers 38 in each of these systems 20 being configured tosend and receive data to and from remote computers and other systems 20.The LAN may be connected to the Internet. This connection allowscontroller 38 to communicate with one or more remote computers connectedto the Internet.

The processors 122 are coupled to memory 124. The memory 124 may includerandom access memory (RAM) device 140, a non-volatile memory (NVM)device 142, and a read-only memory (ROM) device 144. In addition, theprocessors 122 may be connected to one or more input/output (I/O)controllers 146 and a communications circuit 148. In an embodiment, thecommunications circuit 92 provides an interface that allows wireless orwired communication with one or more external devices or networks, suchas the LAN discussed above.

Controller 38 includes operation control methods described herein, whichcan be embodied in application code. For example, these methods areembodied in computer instructions written to be executed by processors122, typically in the form of software. The software can be encoded inany language, including, but not limited to, assembly language, VHDL(Verilog Hardware Description Language), VHSIC HDL (Very High Speed ICHardware Description Language), Fortran (formula translation), C, C++,C#, Objective-C, Visual C++, Java, ALGOL (algorithmic language), BASIC(beginners all-purpose symbolic instruction code), visual BASIC,ActiveX, HTML (Hypertext Markup Language), Python, Ruby and anycombination or derivative of at least one of the foregoing.

In some embodiments, the controller communicates the captured data,i.e., point clouds and images, are captured to a computer 150. Thecomputer 150 can include one or more processors 152 and a memory device154. The computer 150 generates a 3D colorized image by colorizing the3D coordinates in the point clouds using the color images from the 2Dcamera 66. Such colorization requires that the measurement device 100,which is a system of multiple devices, e.g., the 3D scanner 20 and thecamera 66 is calibrated.

FIG. 5 depicts different types of system calibration parametersaccording to one or more embodiments. System calibration parameters 500include the following sets of parameters: camera calibration parameters502, (e.g., 12 parameters); dual camera orientation parameters 504,(e.g., 6 parameters); and multi-device orientation parameters 506, e.g.,orientation of the camera 66 with respect to scanner 20 (e.g., 6parameters). It is understood that the parameters in each of the aboveset of parameters can vary in other embodiments. In some embodiments,the camera calibration parameters 502 are referred to as dc, dx₀, dy₀,k₁, k₂, k₃, p₁, p₂ for frame array cameras with rectilinear lenses or 12parameters for frame array cameras with fisheye lens as follows: dc₁,dc₂, dx₀, dy₀, k₁₁, k₁₂, k₁₃, k₂₁, k₂₂, k₂₃, p₁, p₂. The dual cameraorientation parameters 504 are w₀, φ₀, κ₀, X₀, Y₀, Z₀. Here, (ω₀,φ₀,κ₀)are the relative angular orientation of the left camera coordinatesystem with respect the right camera coordinate system; and (X₀,Y₀,Z₀)are relative position of the projection center of the right camera withrespect to the projection center of the left camera (1202, 1204).Further, the multi-device orientation parameters also include 3 anglesand 3 coordinates ω, φ, κ, X, Y, Z. Here, (ω, φ, κ) are the threerelative rotation angles of the coordinate system of the left camera at66 and the coordinate system of the scanner 20; and (X, Y, Z) are therelative position of the coordinate system of the left camera at 66 andthe coordinate system of the scanner 20.

Reliably calibrating the measurement device 100, i.e., acquiring allsystem calibration parameters 500, and maintaining the calibrationthroughout the use of the measurement device is a technical challenge.The reliability of system calibration depends on the featuredistribution and the number of features available in a 360° field ofview of the me. Another factor influencing the quality and success ofsystem calibration is the number of matched features in the data (e.g.,intensity image) from the 3D scanner 20 and the data (e.g., color image)from the camera 66. Because the type of light captured by the laserintensity image (active: reflectance of laser from the object surface)from the 3D scanner 20 is different from that in the color image(passive: reflective of natural light from the surface) from the camera66, the number of matched features in these two types of captured datacan be smaller than a required predetermined threshold. This can pose atechnical challenge that prevents performing the system calibration.

Technical solutions are described herein to address such technicalchallenges by providing on-the-fly system calibration. The systemcalibration can use two phases. A first phase is a full systemcalibration (FSC), which determines the entire set of system calibrationparameters 500. A second phase is a limited system calibration (LSC),which determines a partial set of the system calibration parameters 500.Both phases can be performed on-the-fly, and independent of each other.

The FSC facilitates to determine the following: full estimation ofcamera calibration parameters 502 (2×12 parameters); full estimation ofdual camera orientation parameters 504, (e.g., 6 parameters); and fullestimation of multi-device orientation parameters 506 (6 parameters). Inthe example case of FIG. 5, FSC can determine 2×12+6+6=36 parameters.FSC does not use initial values to determine the system calibrationparameters 500. Rather, FSC uses feature extraction, feature matching,and on-the-fly control point construction, which is described herein.The feature extraction and matching are performed using a laser colorimage, which is created by stitching the color images captured by thebuilt-in (internal) camera 112 of the laser scanner 20. In someembodiments, the feature extraction and matching uses at least twopanoramic images. The feature extraction and matching are performed withpoint clouds captured by a laser scan that the scanner 20 performs. Thescanner 20 can be configured to use at least a predetermined lowerresolution, for example, 25% of maximum resolution, to facilitate thelaser scan to be captured faster, and yet detect features that can beused for the calibration.

In case of the LSC, a partial set of the system calibration parameters500 are determined on-the-fly. For example, in some embodiments, the LSCfacilitates to determine the following: full or partial estimation ofcamera calibration parameters 502 (fully or partially 2×12 parameters);full estimation of dual camera orientation parameters 504, (e.g., 6parameters); and full estimation of multi-device orientation parameters506 (6 parameters). In the example case of FIG. 5, LSC can determine allor part of 2×12+6+6=36 parameters. LSC is performed to update initialvalues of the system calibration parameters 500 (as opposed todetermining them from scratch in FSC). In some embodiments, the systemcalibration parameters determined by FSC are used as initial values thatare updated by LSC. In some embodiments, while performing LSC, theinitial values of system calibration parameters 500 are used to performa hybrid feature matching, in which a search space domain is constructedto perform the feature matching more efficiently. In some embodiments,the feature extraction and matching for the LSC uses a single panoramicimage. The feature extraction and matching are performed with respect topoint clouds captured in a laser scan by the scanner 20. The scanner 20can be configured to use a second predetermined resolution, for example,20% of maximum resolution, to facilitate the laser scan to be capturedfaster. The second predetermined resolution can be lower than thepredetermined resolution used for the FSC.

Referring now to FIG. 6, an embodiment of a method 200 is shown forgenerating a scan of the environment with the scanner 20. The method 200begins in block 202 where the environment in which the scanner 20 ispositioned is scanned. As described herein, the volume (e.g. the scanarea) around the laser scanner 20 is performed by rotating the rotarymirror 26 relatively quickly about axis 25 while rotating the measuringhead 22 relatively slowly about axis 23, thereby moving the assembly ina spiral pattern. Thus, for each light beam emitted, a distance valueand the angles of the mirror 26 and the measurement head 22 isdetermined. Thus, a 3D coordinate of a point in the environment may bedetermined for each emitted and received light beam. Further, for eachlight beam, an intensity value of the returned light beam is measured.

The light beams are emitted and received as the measurement head 22 isrotated 180 degrees about the axis 23. The method 200 further includes,at block 208, acquiring color images of the environment. In anembodiment, a 2D color image is acquired by the auxiliary imageacquisition device 66. One or more 2D images are acquired using theultrawide-angle lens captures color data in the spherical volumesurrounding the laser scanner 20. In the exemplary embodiment, the 2Dacquired color images are in an RGB color model. In other embodiments,other color models, e.g., cyan, magenta, and yellow (CMY), or cyan,magenta, yellow, and black (CMYK), or any other color model can be used.

Once the 2D color image is acquired, the method 200 includes, at block210, generating a colorized 3D image by mapping the 2D ultrawide-angleimage with the 3D coordinates in the point cloud captured by the scanner20. Such mapping of the 2D ultrawide-angle image with the 3D point cloudis described further herein.

It should be appreciated that the method 200 provides advantages ingenerating enhanced color 3D scans over techniques that use HDR (HighDynamic Range) imaging techniques because of requiring fewer number ofimages to be captured by using an ultrawide-angle field of view.

Physical agents living in complex environments, such as humans andanimals, use two types of visual sensing abilities. One is to focus onobjects with a precise but small retina and the other is to look aroundthe environment with a wide but coarse retina. Both visual sensingmechanisms are used to enable robust and flexible visual behaviors. Inparticular, the wide visual information obtained by looking around isused to monitor wide areas and to avoid undesired situations. If thecomplete surrounding in space can be involved into the perceptionprocess, orientation and navigation in space becomes easier and morereliable.

Typically, a camera's field of view is smaller than the human field ofview, which limits objects from being captured in a single picture. Thistechnical challenge is addressed by using a ultrawide-angle, i.e.,hemispherical or fisheye lens, which creates a wide field of view image.With a ultrawide-angle lens an image of more than 180° angular field ofview can be acquired. Due to the large field of view, it has been usedin many applications with different domains such as forestry, the studyof plant canopies, geodesy to produce a site obstruction diagram forfuture GPS missions, etc.

Technical challenges of using such a ultrawide-angle lens includelateral color, high order distortion (edge compression), loss ofresolution and severe drop-off of illumination at the full field (e.g.,180°), which limit applications of the ultrawide-angle lenses forprecise photogrammetric applications.

Embodiments of the technical solutions described herein address suchtechnical challenges and facilitate using the ultrawide-angle lens toacquire 2D color images and mapping such images to the 3D coordinates inthe point cloud. Further, technical effects and benefits of someembodiments include providing a 3D measurement device 100 that rapidlyacquires 3D coordinates of a collection of points in a scan area withaccurate color information using the single ultrawide-angle 2D colorimage. In one or more embodiments, the auxiliary image acquisitiondevice 66 can be an omnidirectional camera such as a RICOH® THETA®camera for example. The camera 66 can capture a 360° view of theenvironment by capturing two images substantially concurrently. The twoimages may be captured by two ultrawide-angle lenses that are positionedto be facing in opposite directions, each camera capturing a respectivefield of at least 180°. In some cases, the two images that are capturedcan have overlapping portions that can be combined/edited, eitherautomatically or manually. It is understood that above descriptionprovides some examples of the ultrawide-angle lens, and auxiliary imageacquisition device 66 that can be used in one or more embodiments, andthat in other embodiments, different lenses and/or cameras can be used.

FIG. 7 depicts determining coordinates corresponding to pixelsrepresenting objects/surfaces captured by an ultrawide-angle imageaccording to one or more embodiments. A difference between anultrawide-angle lens and a typical rectilinear lens is that theprojection from a 3D point to a 2D image in the ultrawide-angle lens isintrinsically non-perspective. Depending on the amount of deviation ofthe ray, equations below, and FIG. 7 provide four different types ofprojections which characterize ultrawide-angle lenses:

$\begin{matrix}{{Equidistant}{projection}(610):} & {r_{d} = {c.\theta}} \\{{Orthographic}{projection}(620):} & {r_{d} = {c.{\sin(\theta)}}} \\{{Equisolid} - {angle}{projection}(630):} & {r_{d} = {2{c.{\sin( \frac{\theta}{2} )}}}} \\{{Stereographic}{projection}(640):} & {r_{d} = {2{c.{\tan( \frac{\theta}{2} )}}}}\end{matrix}$

Here, κ is the zenith angle, c is a camera constant (in millimeter orpixels), and rd is the radius of the image point P (from the principalpoint). FIG. 8 shows the relation of radius and zenith angle (andreverse) for perspective projection and the four ultrawide-angle lensprojection types 610, 620, 630, 640. The plot 710 shows that a lens withperspective projection requires an infinite image plane to projectionnear (and less) than 180° field of view. The plot 720 shows thatorthographic projection type cannot handle field of view near (and more)than 180°. Typically, lenses available are designed to produce anequidistant projection. For example, NIKON® 8-mm f/2.8, CANON® 7.5-mmf/5.6, SIGMA® 15-mm f/2.8 (180° FOV), NIKON® 6-mm (220° FOV), and RICOH®THETA® ultrawide-angle lens 2.6-mm (˜204° FOV) are examples ofequidistant projection ultrawide-angle lenses.

Collinearity equations represent a set of two equations, used inphotogrammetry and remote sensing to relate coordinates in a sensorplane (in two dimensions) to object coordinates (in three dimensions).Equation (1) represents collinearity equations for a 2D ultrawide-anglelens as used in one or more embodiments:

$\begin{matrix}\begin{matrix}{x = {- {\frac{c}{m}.\frac{U_{X}}{U_{Z}}}}} \\{y = {- {\frac{c}{m}.\frac{U_{Y}}{U_{Z}}}}}\end{matrix} & (1)\end{matrix}$

Here, (x, y) is the image point coordinates in the photo coordinatesystem (e.g., millimeter or pixels), c is the camera constant, m is aultrawide-angle lens coefficient factor (unit free). The (U_(X), U_(Y),and U_(Z)) are intermediate values that can be computed as follows:

$\begin{pmatrix}U_{X} \\U_{Y} \\U_{Z}\end{pmatrix} = {R^{t}.\begin{pmatrix}{X - X_{0}} \\{Y - Y_{0}} \\{Z - Z_{0}}\end{pmatrix}}$

Here, (X₀, Y₀, Z₀) is the position of the center of projection (see FIG.7), and (X, Y, Z) is the resulting object point coordinates in 3D space,and R=R_(X) R_(Y) R_(Z), in which:

${R_{X} = \begin{pmatrix}1 & 0 & 0 \\0 & {\cos(\omega)} & {{- s}{in}(\omega)} \\0 & {\sin(\omega)} & {\cos(\omega)}\end{pmatrix}},{R_{Y} = \begin{pmatrix}{\cos(\varphi)} & 0 & {\sin(\varphi)} \\0 & 1 & 0 \\{{- s}{in}(\varphi)} & 0 & {\cos(\varphi)}\end{pmatrix}},{{{and}R_{Z}} = \begin{pmatrix}{\cos(\kappa)} & {{- s}{in}(\kappa)} & 1 \\{\sin(\kappa)} & {\cos(k)} & 1 \\0 & 0 & 1\end{pmatrix}},$

where (ω, φ, κ) are the three rotation angles around the X, Y, and Zaxes respectively. The point at coordinates (X, Y, Z) in the 3D pointcloud is mapped and colorized with the pixel at (x, y) from the 2Dultrawide-angle image as a result of the above calculations.

Only the equidistant projection (610), and the equisolid-angleprojection (630) types can properly model the Ricoh Thetaultrawide-angle lens. Accordingly, examples described herein provideequations that are applicable for those two projection models, however,it is understood that other types of projection models can be usedwithout significant changes to the description provided herein. Forexample, the following are the calculations for the lens coefficient, m,for the equidistant and the equisolid-angle projection types for theultrawide-angle lens:

$\begin{matrix}{{{Equidistant}{projection}{coefficient}:m} = {- \frac{\tan(\theta)}{0}}} \\{{{Equisolid} - {angle}{coefficient}:m} = {- \frac{\tan(\theta)}{2.{\sin( \frac{\theta}{2} )}}}}\end{matrix}$

It should be noted that in the case of the ultrawide-angle lens of theauxiliary image acquisition device 66, the range of θ is [0, π]. Amongthe trigonometric functions, only the inverse of cosine or the inverseof cotangent return the angle in the range of [0, π] (for inverse ofcotangent is (0, π)). Accordingly, one or more embodiments use theinverse of cosine (a cos) to determine the angle theta. Inverse of sineor inverse of tangent do not have this property. If they are used in theformulation, they cannot determine the sign and the value of θ forincoming rays with θ near to π/2 or larger than π/2 (FOV of near to π orlarger than π). FIG. 9 depicts a table 810 that provides the domains andranges of various trigonometric functions. Based on these, the abovedescribed calculations of the camera coefficient m are based on usingthe following computation for the angle θ:

$\theta = {\cos^{- 1}( {- \frac{U_{Z}}{\sqrt{U_{X}^{2} + U_{Y}^{2}}}} )}$

The above described calculation resolves the ambiguity of mapping the 3Dpoint cloud captured by the laser scanner 20 to pixels from the 2Dultrawide-angle color image from the auxiliary image acquisition device66 at near to or larger than zenith angle of 90°. By using the abovetechniques for calculating the angle θ embodiments described hereineliminate disambiguation of the sign and value of the angle θ.Therefore, the coefficient m and the further calculations that use m arecalculated correctly

Typically, during mapping an image to a point cloud, straight lines inthe real world (i.e., point cloud) are mapped to straight lines in theimage generated by the rectilinear camera. However, most real opticalsystems introduce some undesirable effects, rendering the assumption ofthe rectilinear camera model inaccurate. In the case of the auxiliaryimage acquisition device 66, a radial distortion (also referred to as“radial barrel distortion”) causes points on the image plane to beshifted from their ideal position along a radial axis from the principalpoint in the ultrawide-angle image plane. The visual effect of thisdisplacement in ultrawide-angle optics is that the image has a higherresolution in the foveal areas, with the resolution decreasingnonlinearly toward the peripheral areas of the image.

Typically, the following set of equations are used to determinecorrection terms to image point coordinates. The equations useadditional parameters for modeling the systemic errors of frame arraycameras with rectilinear lenses.

$\begin{matrix}\begin{matrix}\begin{matrix}{{\Delta x} = {{dx_{0}} - {\overset{¯}{\frac{x}{c}}dc} - {S_{x}\overset{¯}{x}} + {a\overset{¯}{y}} + {\overset{¯}{x}( {{r^{2}k_{1}} + {r^{4}k_{2}} + {r^{6}k_{3}}} )} + {( {r^{2} + {2\overset{¯}{x}}} )p_{1}} +}} & {2\overset{\_}{x}\overset{\_}{y}p_{2}}\end{matrix} \\\begin{matrix}{{\Delta y} = {{dy_{0}} - {\frac{\overset{\_}{y}}{c}dc}}} & {{{+ a}\overset{¯}{x}} + {\overset{¯}{y}( {{r^{2}k_{1}} + {r^{4}k_{2}} + {r^{6}k_{3}}} )} +} & {{2\overset{¯}{x}\overset{¯}{y}p_{1}} + {( {r^{2} + {2\overset{¯}{y}}} )p_{2}}}\end{matrix}\end{matrix} & (2)\end{matrix}$

Here, dc is a correction to camera constant c, (dx₀, dy₀) representscorrections to the shift of principal point (x₀, y₀), (S_(x), a) areaffine transformation parameters: scale difference and shear, k₁, k₂, k₃are parameters of radial lens distortion, and p₁, p₂ are parameters ofdecentering lens distortion.

It is known, that in modern electronic sensor manufacturing, the terms(S_(x), a) are negligible therefore, equations (2) consists of 8 cameracalibration parameters which are determined through the process ofcamera self-calibration. A technical challenge is that such cameracalibration parameters introduce systemic errors that inhibit theaccurate colorizing of the 3D point cloud using the single 2Dultrawide-angle color image. It should be noted that although eightcamera calibration parameters are depicted in equations (2), in otherembodiments there can be a different number of camera calibrationparameters.

Embodiments herein address such technical challenges by facilitatingsystem calibration 500.

Typically, system camera calibration through self-calibration can beperformed in three ways: first, block triangulation with free network;second, block triangulation with object space constraints for example,control points or 3D straight lines; and third, space resection ofindividual images using control points. In the first and secondapproach, using bundle adjustment, the camera calibration parameters,the exterior orientation parameters of images, and the position of theobject points are estimated simultaneously through aleast-squares-optimization approach. Here, “bundle adjustment” is aknown algorithm or process that is used in 3D construction techniques.Given a set of images depicting a number of 3D points from differentviewpoints, bundle adjustment can be defined as the problem ofsimultaneously refining the 3D coordinates describing the scenegeometry, the parameters of the relative motion, and the opticalcharacteristics of the camera(s) employed to acquire the images,according to an optimality criterion involving the corresponding imageprojections of all points.

In the third approach, the control points are used, and the systemcalibration parameters 500 which are the camera calibration parameters,and the external orientation parameters of the images are estimatedsimultaneously. In the case of the setup of the 3D scanner 20 and theauxiliary image acquisition device 66, the latter being fixed to the 3Dscanner 20, the system camera calibration has to be performed using thethird approach that uses space resection of individual images usingcontrol points. This technique is now described.

Typically, in photogrammetry, a test-field of control points is built inorder to calibrate the system 500 including a laser scanner 20 and acamera 66, and to perform an accuracy testing of the calibrationprocedure. This control point test-field is typically measured with atechnique, which has a better positioning accuracy compared to theintended accuracy that is aimed to be achieved after the systemcalibration. Then, using the bundle adjustment process the systemcalibration parameters 500 which includes the camera calibrationparameters and exterior orientation parameters of the images areestimated simultaneously.

However, in the case of the 3D scanner 20 with the fixed auxiliary imageacquisition device 66, the system has to be calibrated at locationswhich are not in a control point test-field. Therefore, using atest-field of control points to perform a regular system calibration fordata captures at locations where the 3D scanner 20 is going to be usedis not a practical approach. Accordingly, embodiments described hereinfacilitate dynamically building a test-field of control points by usinga point cloud that is captured by the 3D scanner 20. The control pointsof this test-field are selected points from the point cloud.

FIG. 10 depicts a flowchart for a dynamic construction of control pointsfor system calibration according to one or more embodiments. The method900 includes capturing a point cloud using the 3D scanner 20, at block902. Further, a control image is captured using the camera 112 that isintegrated with the 3D scanner 20, at block 904. In one or moreembodiments, multiple images are captured using the integrated camera112, and the images are stitched together. Alternatively, in the casethat the 3D scanner 20 does not have an integrated camera 112, anintensity image is captured and used as the control image. The intensityimage does not have color information (e.g., Red, Green, Blue (RGB), orCyan, Magenta, Yellow (CMY) etc.), rather has light intensityinformation at each captured pixel in the image. Further, calibrationimages are captured by the auxiliary image acquisition device 66, whichis to be calibrated, at block 906.

Method 900 further includes extracting natural features in all of theimages that are captured, at block 908. All the images here include thecontrol image taken by the internal camera 112 (or the intensity image)and the calibration images taken by the auxiliary image acquisitiondevice 66. Feature extraction can be performed using one or more knownalgorithms such as, Harris corner detector,Harris-Laplace-scale-invariant version of Harris detector, multi-scaleoriented patches (MOPs), scale invariant feature transform (SIFT),speeded up robust features (SURF), Features from accelerated segmenttest (FAST), binary robust invariant scalable key-points (BRISK)algorithm, oriented FAST and rotated BRIEF (ORB) algorithm, KAZE withM-SURF descriptor, and any other feature extraction technique. Some ofthe feature extraction techniques such as, SIFT, SURF, BRISK and ORBalso provide descriptors for the extracted features. Alternatively, orin addition, any feature descriptor definition can be associated to theextracted features. For example, the following descriptor definitionscan be used: normalized gradient, principal component analysis (PCA)transformed image patch, histogram of oriented gradients, gradientlocation and orientation histogram (GLOH), local energy-based shapehistogram (LESH), BRISK, ORB, fast retina key-point (FREAK), and localdiscriminant bases (LDB).

In an embodiment, the feature extraction is based on a modified AKAZEalgorithm which is executed on a graphics processing unit (GPU) toincrease runtime efficiency. The descriptors assigned to the extractedfeatures are the modified version of the M-SURF descriptors. The featureextraction results include a collection of points from each image, eachpoint in the collection being an extracted “feature.” The criteria forextracting such features can include detecting semantic features fromthe images such as, corners, edges, doors, windows, etc. Alternatively,or in addition, the feature extraction can include detecting points thatprovide combinations of parameters that facilitate reducing the numberof features required to processed for effective feature matching. Forexample, such feature dimensionality reduction can include techniquessuch as principal component analysis (PCA), autoencoder, subspacelearning, semidefinite embedding, isomap, partial least squares, etc.

Typically, corresponding regions in the ultrawide-angle imagecalibration image from the auxiliary image acquisition image 66 and thecontrol images (color or intensity) from the scanner 20 have large localgeometrical deformation differences. It is due to different geometricalprojection models. FIG. 11 shows a control image 1000 (color image) andthe ultrawide-angle image 1010 and for example, two corresponding imageregions 1002, 1012 with geometrical difference. These deformationdifferences limit the performance of feature matching and can result ina limited number of matching features particularly around the rim ofultrawide-angle image 1010.

To address such a technical challenge, i.e., to reduce/eliminate thelocal deformation difference at corresponding regions, theultrawide-angle image 1010 is converted to a spherical image 1020. Thistransformation is based on the ultrawide-angle sensor model, describeherein, and using a mapping between the spherical image 1020 and theultrawide-angle image 1010. By this transformation, the differences oflocal deformation at corresponding regions are minimized (comparing 1000and 1020). Therefore, as a result the feature descriptors become moresimilar and more features are matched especially around nadir and zenithof the control image 1010.

In order to establish the mapping function from the ultra-wide-angleimage points (x, y) to spherical image pixels (col, row), the spacevector of the pixels of the ultra-wide-angle image is computed asfollows:

$s = \begin{pmatrix}{x + {\Delta x}} \\{y + {\Delta y}} \\{- \frac{c}{m}}\end{pmatrix}$

in which, x, y, c, and m are defined in equation (1) and Δx, Δy aredefined in equation (2). The space vector is then normalized

$( {s = \frac{s}{s}} ).$

“s” is in the 3D Cartesian coordinate system with unit length. Byconverting the Cartesian coordinate system to Polar coordinate system,(θ, φ) are computed. θ is the azimuth angle with a range from [0, 360°].φ is the zenith angle having a range [θ, 90°]. The pixel in thespherical image (col, row) is computed by dividing (θ,φ) to the pitchangle. The pitch angle is computed by dividing the pixel pitch of theexternal camera to its camera constant.

Further, the method 900 includes matching the features that areextracted across all of the images, at block 910. For a full systemcalibration (FSC), in which the control panorama image is the stitchedimages of the built-in camera, a K-nearest neighbor (KNN) similaritysearch algorithm can be used for feature matching. KNN similarity searchalgorithm is a non-parametric method used for classification andregression. The process of feature matching is time consuming. Hence, tospeed-up the computation approximated nearest neighbor search like theFLANN algorithm can be performed in one or more embodiments. FLANN is alibrary for performing fast approximate nearest neighbor searches inhigh dimensional spaces. It should be noted that the feature matching isnot limited to a specific algorithm, and that in other embodiments, thefeature matching can be performed by executing algorithms that can berun on GPU like those in the libraries like FAISS, etc. For a limitedsystem calibration (LSC), in which the control panorama image is thelaser intensity image, a hybrid feature matching is used (which isdescribed in a co-pending application, docket number P1693-US).

FIG. 12 depicts corresponding features between the color image 1000 andthe ultrawide-angle image 1010. The resulting match 1100 is a result ofmatching features extracted from the ultrawide-angle image 1010 and thecontrol image 1000. In the depicted example, 694 features are matched byusing the ultrawide-angle image 1010 and the control image 1000. In thesecond case, the resulting match 1110 is a result of matching featuresextracted from the control image 1000 and the spherical image 1020,which is obtained by transforming the ultrawide-angle image 1010 to thespherical image space. Here, the resulting match 1110 includes 1657matching features. It is understood that the number of features that areextracted and matched can be different in different embodiments based onthe extraction technique and the matching technique that is used. Itshould be noted that the matched features from the spherical image 1020have been transferred to the ultrawide-angle image 1010 using a reversetransformation from the spherical image space to the ultrawide-angleimage space.

At block 912, the 3D coordinates of matched features are estimated usingthe 3D point cloud. The control image, the calibration image, and the 3Dpoint cloud are captured by the camera 112, the auxiliary imageacquisition device 66, and the 3D scanner 20, respectively, from thesame position/location in 3D space. Because the extracted features havesub-pixel accuracy, estimating their 3D coordinates requires more thanpicking a corresponding point to the feature from the point cloud. In anembodiment, a bilinear interpolation is used to estimate the 3Dcoordinates of the matching features. Bilinear interpolation is anextension of linear interpolation for interpolating functions of twovariables (e.g., x and y) on a rectilinear 2D grid. Bilinearinterpolation is performed using linear interpolation first in onedirection, and then again in the other direction. Although each step islinear in the sampled values and in the position, the interpolation isnot linear but rather quadratic in the sample location.

In an example, the control image 1000, which is captured by the internalcamera 112 is mapped with the 3D point cloud. The 3D coordinate of thematched feature is estimated by identifying the pixel/sub-pixel wherethe matched feature maps. As noted earlier, if the matched feature mapsto a sub-pixel, the surrounding coordinates are used to perform thebilinear interpolation to determine the 3D coordinate of the matchedfeature.

It should be noted that other techniques can also be used for estimatingthe 3D coordinates of the features. For example, other interpolationapproaches like bicubic interpolation can be used in other embodiments.The 3D coordinates of the features are stored as the control points.

Referring to the flowchart in FIG. 10, at block 914, system calibrationis done on-the-fly by using a numerical approach using “bundleadjustment” with camera self-calibration. Given a set of imagesdepicting a number of 3D points from different viewpoints, “bundleadjustment” can be defined as the problem of simultaneously refining the3D coordinates describing the scene geometry, the parameters of therelative motion, and the optical characteristics of the camera(s)employed to acquire the images, according to an optimality criterioninvolving the corresponding image projections of all points. In order toimprove the reliability of bundle adjustment, embodiments herein extendthe bundle adjustment by using image clusters. Here, the unknowns, i.e.,the calibration parameters of the auxiliary image acquisition device 66,are estimated simultaneously for a cluster of images using least squaresoptimization approach.

An image cluster is defined as a group of images. They have a constantrelative orientation among each other. For example, an image cluster canbe defined by the left and right images captured by a dual camera 66(FIG. 5), such as RICOH THETA®. The dual camera includes a left camera1202, and a right camera 1204. It is understood that the two cameras1202, 1204, can be labeled using any other labels, such as first camera,and second camera, etc. Each image cluster includes at least one imagecaptured by the left camera 1202, and a corresponding image captured bythe right camera 1204. Each image cluster has at least six exteriororientation parameters that according to which the cluster is orientedand positioned in 3D space. These 6 parameters are equivalent to dualcamera orientation parameters at 504. Based on the geometry of the dualcamera 66, at least three conditions can be imposed to the relativeposition of the dual camera 66:

ΔX=0, the two cameras (left and right) are at the same X-coordinateposition; ΔY=0, the two cameras (left and right) are at the sameY-coordinate position; and ΔZ=z mm, the two cameras (left and right)have a spacing distance equal to z mm. Here, the above values can beconfigured according to the geometry of the auxiliary image acquisitiondevice 66. For example, z=15 mm, 19 mm, 27 mm, etc.; x=0, 5 mm, 10 mm,etc.; and y=0, 5 mm, 10 mm, etc.

Based on the conditions, the bundle adjustment is modified inembodiments herein, so that the relative orientation parameters of theimages of a cluster and the exterior orientation parameters of the imageclusters are estimated simultaneously together with camera calibrationparameters. The exterior orientation parameters of the image clustersare equivalent to multi-device orientation parameters at 506. Ifrelative orientation of the images of a cluster are known in advance,they can be used as constraints or conditions.

In one or more embodiments, the calibration and the configuration of thesystem calibration parameters 500 is performed using the measurementdevice 100 in the field. This improves the efficiency of the usage ofmeasurement device 100. Without the features described herein, themeasurement device 100, with the auxiliary image acquisition device 66,had to be pre-calibrated in a controlled environment.

In one or more embodiments of the present invention, once the system iscalibrated using method 900, the measurement device 100 can becalibrated to determine one or more correction factors based on thesensor model of the auxiliary image acquisition device 66. Thecorrection factors are subsequently applied to each of the 2Dcoordinates of the images acquired by the auxiliary image acquisitiondevice 66.

The corrected coordinates of the image together with exteriororientation of images of the external camera 66 are used during thecolorization of the 3D point cloud in the method 200 (block 210).Accordingly, the 3D image generated by the measurement device 100 iscolorized more accurately by embodiments described herein compared toexisting solutions.

FIG. 13 depicts an example result from embodiments described herein. Inan outdoor setting control images 1301, 1302 are captured as laserpanoramas. A color image 1301 is computed by stitching the images takenfrom the built-in camera 112 of the laser scanner 20. Further, a laserintensity image 1302 can be measured directly by the laser scanner 20.Further shown are calibration images 1303, 1304 taken by the auxiliaryimage acquisition device 66, in this case, a left image 1303, and aright image 1304 captured by a dual camera (FIG. 5). The constructedcontrol points based on these images are shown in a side view 1305, anda top view 1306. All system calibration parameters at 500 including thecamera calibration parameters 502, the dual camera orientationparameters 504, and the multi-device orientation parameters 506 wereestimated successfully by using the control points and the bundleadjustment by using image clusters, as described herein. It should benoted that although an outdoor setting is shown in the example resultsin FIG. 13, embodiments herein address the technical challengesdescribed herein in indoor settings as well.

Embodiments described herein facilitate system calibration, which worksbased on space resection using a test-field of control points.Embodiments described herein facilitate the test-field of control pointsto established dynamically, at the worksite (field). Further, to addresstechnical challenges, embodiments herein also modify feature extractionto increase number of control points that can be extracted, andconsequently that can be matched between control images and calibrationimages. Further, bundle adjustment is modified by using image clustersin order to handle additional constraints/conditions that are to beimposed because of a dual camera with ultrawide-angle lenses.

As a result, embodiments described herein address the technical problemof system calibration reliably, even in scenarios that typically have alow number of control points, and inhomogeneous distribution of controlpoints in 360° field of view provided by an ultrawide-angle lens.Accordingly, using embodiments described herein, with typically oneimage from each camera (1202, 1204) in a dual camera (1200), the systemcalibration parameters can be estimated reliably and accurately.

The accuracy of control points resulting from embodiments describedherein is limited only by the accuracy of stitching the images of theinternal camera 112 of the laser scanner 20 to generate a control image1000. The reliability of embodiments described herein depends on thefeature distribution and the number of features that can be extracted in360° field of view of the ultrawide-angle lenses. The results fromseveral example studies (e.g., FIG. 13) have shown that the number offeatures and the distribution facilitate a reliable and accurate cameracalibration using embodiments described herein.

FIG. 14 depicts a flowchart of a method for using data captured by ameasurement device using on-the-fly system calibration according to oneor more embodiments. FIG. 15 provides a visual depiction of an exampletimeline of a workflow for performing the method 1400.

The method 1400 includes performing FSC of the measurement device 100,at block 1402. The FSC is performed at a predetermined frequency, suchas every month, 2 or 4 months, etc. Typically, the FSC will be performedbefore the very first use of the measurement device 100. In the workflowof FIG. 15, at timepoint T0, an FSC is performed to determine all thesystem calibration parameters 500.

As noted earlier, FSC determines all the system calibration parameters500, and accordingly, can take longer than performing LSC. The reasonsFSC takes longer includes that it requires more data to be captured. TheFSC requires a laser scan, at least two panoramic images from the camera66, and a control image (multiple images) from the internal camera 112.Using this captured data, all the system calibration parameters 500 arecomputed as described herein.

Further, to generate the 3D image of the environment using thecalibrated measurement device, the scanner 20 captures the pointcloud(s) and the camera 66 captures the corresponding ultrawide-angleimage, at bock 1404. The computer 150 receives the captured data andperforms an LSC, at 1406. As noted earlier, LSC only computes a partialset of the system calibration parameters 500, using only the 3D scan andthe ultrawide-angle image. The LSC updates the partial set of the systemcalibration parameters 500, if the computation is successful.

The updated system calibration parameters 500 are used for colorizingthe captured point cloud from the scanner 20 using the ultrawide-angleimage from the camera 66 using the mapping formulae described herein.(see equation (1)), at block 1408.

At block 1410, it is determined whether a full system recalibration isrequired. For example, after the predetermined duration, and/or afterthe predetermined number of uses of the measurement device 100, thesystem calibration changes. Alternatively, the system calibration canchange because of user errors, accidental changes, or any other suchreasons.

Referring to the example scenario in FIG. 15, at timepoint T1 it isdetermined that a FSC is not required. Accordingly, the 3D imagegeneration is performed, where an LSC is performed and the updatedsystem calibration parameters 500 from the LSC are used to colorize thecaptured point cloud. Timepoints T1-Tn are performed in the similarmanner. For example, T1-Tn can represent the predetermined duration, thepredetermined number of iterations, or iterations until the colorizationdoes not result in a distortion beyond a predetermined threshold. Attimepoint Tn+1, a full system recalibration is deemed to be required,and another FSC is performed for the measurement device 100. The FSCdetermines a new set of system calibration parameters 500. The abovesteps can be performed continuously over time.

FIG. 16 depicts an example scenario where a portion 1504 of the pointcloud 1502 colorized using an ultrawide-angle image 1506 from camera 66results in a distorted view. In this particular example scenario, thecoloring is performed using the system calibration parameters 500computed by an FSC that was performed 3 weeks earlier. In this case, thealignment errors in the portion 1504 are seen because of change of thesystem calibration parameters 500 (aging effect). In other embodiments,the system calibration parameters can change because of other reasons,and the alignment errors can be different than those depicted in FIG.16.

Technical solutions described herein, by using the LSC before eachcolorization, avoids such distortion. FIG. 17 depicts a result ofcolorization of the same example scene of FIG. 16 using the technicalsolutions described herein. As can be seen the distortions (from FIG.16) in the portions 1504 are not visible in the case of FIG. 17.

Embodiments described herein, accordingly provide a practicalapplication to improve operation of a 3D measurement device,particularly a 3D scanner that uses an auxiliary image acquisitiondevice equipped with an ultrawide-angle lens. Embodiments describedherein facilitate the on-the-fly system calibration using control pointsthat are generated dynamically, at runtime, on the worksite, withoutrequiring expensive and time-consuming steps of setting up a controlenvironment. Such camera calibration is the pre-requisite of all 3Dmeasurement applications.

The technical solutions described herein creates significant timesavingand flexibility for the user. The technical solutions provide animprovement to computing technology of 3D measurement devices, andparticularly colorizing point clouds captured by a 3D scanner using anultrawide-angle image. The improvements include requiring lesser datacollection compared to performing an FSC at a higher frequency. Thetechnical solutions described herein provide a practical application anddoes not require additional data for the improvement, because an LSCuses the same data that is captured for generating the colorized pointcloud (with or without an LSC).

Apart from the system calibration process, embodiments described hereincan be used within the process of coloring a point cloud that iscaptured by the scanner 20, at least in the following modes: staticscanning, and dynamic scanning (e.g., FARO® SWIFT®).

It should be appreciated that while embodiments herein describe thereduction of the image point residuals with reference to the use of thecamera with the ultrawide-angle lens and a three-dimensional scanner,this is for example purposes and the claims should not be so limited. Inother embodiments, the residual reduction could be used in otherapplications that use an omnidirectional camera, or a camera with asingle ultrawide-angle lens to improve the accuracy of the image.

Terms such as processor, controller, computer, DSP, FPGA are understoodin this document to mean a computing device that may be located withinan instrument, distributed in multiple elements throughout aninstrument, or placed external to an instrument.

While the invention has been described in detail in connection with onlya limited number of embodiments, it should be readily understood thatthe invention is not limited to such disclosed embodiments. Rather, theinvention can be modified to incorporate any number of variations,alterations, substitutions or equivalent arrangements not heretoforedescribed, but which are commensurate with the spirit and scope of theinvention. Additionally, while various embodiments of the invention havebeen described, it is to be understood that aspects of the invention mayinclude only some of the described embodiments. Accordingly, theinvention is not to be seen as limited by the foregoing description butis only limited by the scope of the appended claims.

1-20. (canceled)
 21. A system comprising: a three-dimensional (3D)scanner that captures a 3D point cloud that comprises a plurality of 3Dcoordinates corresponding to one or more objects scanned in asurrounding environment; a camera that captures a control image bycapturing a plurality of images of the surrounding environment, whereinimages from the plurality of images are stitched to form the controlimage; an auxiliary camera configured to capture an ultrawide-angleimage of the surrounding environment; and one or more processorsconfigured to colorize the 3D point cloud using the ultrawide-angleimage by mapping the ultrawide-angle image to the 3D point cloud, andwherein, the one or more processors are further configured to: perform afull system calibration to calibrate 3D scanner, the camera, and theauxiliary camera using the 3D point cloud, the control image, and acalibration image, which is another ultrawide-angle image from theauxiliary camera, wherein the full system calibration comprises:extracting a first plurality of features from the control image using afeature-extraction algorithm; extracting a second plurality of featuresfrom the calibration image using the feature-extraction algorithm;determining a set of matching features from the first plurality offeatures and the second plurality of features by using afeature-matching algorithm; building control points by using the set ofmatching features and the 3D point cloud; and determining all systemcalibration parameters on-the-fly using bundle adjustment and cameraself-calibration, and perform a limited system calibration beforecolorizing the 3D point cloud, wherein the limited system calibrationcomprises updating a subset from the set of system calibrationparameters, wherein limited system calibration comprises: extracting athird plurality of features from the control image using thefeature-extraction algorithm; determining another set of matchingfeatures to the extracted features at the first plurality of features byusing a hybrid feature matching algorithm; building control points byusing the another set of matching features and the 3D point cloud; anddetermining updated values for at least one of the system calibrationparameters on-the-fly through bundle adjustment and cameraself-calibration.
 22. The system of claim 21, wherein the limited systemcalibration is performed before colorizing each 3D point cloud, and thefull system calibration is performed after a plurality of 3D pointclouds are colorized.
 23. The system of claim 22, wherein two successivefull system calibrations are performed after a predetermined interval.24. The system of claim 22, wherein successive full system calibrationsare performed after a predetermined number of 3D point clouds arecolorized.
 25. The system of claim 21, wherein extracting the secondplurality of features from the calibration image comprises: transformingthe calibration image to a spherical image; and extracting the secondplurality of features from the spherical image.
 26. The system of claim21, wherein the auxiliary camera includes two lenses at predeterminedoffsets relative to each other.
 27. The system of claim 21, wherein theset of system calibration parameters includes a first plurality ofcamera calibration parameters, a second plurality of dual-cameracalibration parameters, and a third plurality of multi-deviceorientation parameters.
 28. The system of claim 21, wherein theauxiliary camera is mounted on the 3D scanner at a predeterminedposition relative to the 3D scanner.
 29. A method comprising:periodically performing a full system calibration of a measurementdevice that comprises a 3D scanner, a camera, and an auxiliary camera,wherein the full system calibration comprises: extracting a firstplurality of features from a control image using a feature-extractionalgorithm, the control image is captured by the camera by capturing andstitching a plurality of images; extracting a second plurality offeatures from a calibration image using the feature-extractionalgorithm, the calibration image being an ultrawide-angle image from theauxiliary camera; determining a set of matching features from the firstplurality of features and the second plurality of features by using afeature-matching algorithm; building control points by using the set ofmatching features and the 3D point cloud; and determining all systemcalibration parameters on-the-fly using bundle adjustment and cameraself-calibration, and performing a limited system calibration of themeasurement device in response to capturing a second 3D point cloud thatis to be colorized, wherein the limited system calibration comprisesupdating a subset from the set of system calibration parameters usingthe second 3D point cloud and a second ultrawide-angle image from theauxiliary camera, wherein limited system calibration comprises:extracting a third plurality of features from the control image usingthe feature-extraction algorithm; determining another set of matchingfeatures to the extracted features at the first plurality of features byusing a hybrid feature matching algorithm; building control points byusing the another set of matching features and the second 3D pointcloud; and determining updated values for at least one of the systemcalibration parameters on-the-fly through bundle adjustment and cameraself-calibration.
 30. The method of claim 29, wherein two successivefull system calibrations are performed after a predetermined interval.31. The method of claim 29, wherein extracting the second plurality offeatures from the calibration image comprises: transforming theultrawide-angle image to a spherical image; and extracting the secondplurality of features from the spherical image.
 32. The method of claim29, wherein the auxiliary camera includes two lenses at predeterminedoffsets relative to each other.
 33. The method of claim 29, whereinsuccessive full system calibrations are performed after a predeterminednumber of 3D point clouds are colorized.
 34. The method of claim 29,wherein determining the points in the 3D point cloud that arecorresponding to the set of matching features is performed usingbilinear interpolation.
 35. A computer program product comprising one ormore memory devices with computer executable instructions storedthereon, the computer executable instructions when executed by one ormore processors cause the one or more processors to perform a methodcomprising: periodically performing a full system calibration of ameasurement device that comprises a 3D scanner, a camera, and anauxiliary camera, wherein the full system calibration comprises:extracting a first plurality of features from a control image using afeature-extraction algorithm, the control image is captured by thecamera by capturing and stitching a plurality of images extracting asecond plurality of features from a calibration image using thefeature-extraction algorithm, the calibration image being anultrawide-angle image from the auxiliary camera determining a set ofmatching features from the first plurality of features and the secondplurality of features by using a feature-matching algorithm; buildingcontrol points by using the set of matching features and the 3D pointcloud; and determining all system calibration parameters on-the-flyusing bundle adjustment and camera self-calibration, and performing alimited system calibration of the measurement device in response tocapturing a second 3D point cloud that is to be colorized, wherein thelimited system calibration comprises updating a subset from the set ofsystem calibration parameters using the second 3D point cloud and asecond ultrawide-angle image from the auxiliary camera, wherein limitedsystem calibration comprises: extracting a third plurality of featuresfrom the control image using the feature-extraction algorithm;determining another set of matching features to the extracted featuresat the first plurality of features by using a hybrid feature matchingalgorithm; building control points by using the another set of matchingfeatures and the second 3D point cloud; and determining updated valuesfor at least one of the system calibration parameters on-the-fly throughbundle adjustment and camera self-calibration.
 36. The computer programproduct of claim 35, wherein two successive full system calibrations areperformed after a predetermined interval.
 37. The computer programproduct of claim 35, wherein successive full system calibrations areperformed after a predetermined number of 3D point clouds are colorized.38. The computer program product of claim 36, wherein extracting thesecond plurality of features from the calibration image comprises:transforming the ultrawide-angle image to a spherical image; andextracting the second plurality of features from the spherical image.39. The computer program product of claim 35, wherein determining thepoints in the 3D point cloud that are corresponding to the set ofmatching features is performed using bilinear interpolation.
 40. Thecomputer program product of claim 35, wherein the set of systemcalibration parameters includes a first plurality of camera calibrationparameters, a second plurality of dual-camera calibration parameters,and a third plurality of multi-device orientation parameters.